On Automatic Data Augmentation for 3D Point Cloud Classification
- URL: http://arxiv.org/abs/2112.06029v1
- Date: Sat, 11 Dec 2021 17:14:16 GMT
- Title: On Automatic Data Augmentation for 3D Point Cloud Classification
- Authors: Wanyue Zhang, Xun Xu, Fayao Liu, Chuan-Sheng Foo
- Abstract summary: We propose to automatically learn a data augmentation strategy using bilevel optimization.
An augmentor is designed in a similar fashion to a conditional generator and is optimized by minimizing a base model's loss on a validation set.
We evaluate our approach on standard point cloud classification tasks and a more challenging setting with pose misalignment between training and validation/test sets.
- Score: 19.338266486983176
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Data augmentation is an important technique to reduce overfitting and improve
learning performance, but existing works on data augmentation for 3D point
cloud data are based on heuristics. In this work, we instead propose to
automatically learn a data augmentation strategy using bilevel optimization. An
augmentor is designed in a similar fashion to a conditional generator and is
optimized by minimizing a base model's loss on a validation set when the
augmented input is used for training the model. This formulation provides a
more principled way to learn data augmentation on 3D point clouds. We evaluate
our approach on standard point cloud classification tasks and a more
challenging setting with pose misalignment between training and validation/test
sets. The proposed strategy achieves competitive performance on both tasks and
we provide further insight into the augmentor's ability to learn the validation
set distribution.
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